Perbandingan Naïve Bayes dan Support Vector Machine untuk Klasifikasi Ulasan Pelanggan Indihome

Aan Rohanah; Dwi Latifah Rianti; Betha Nurina Sari

doi:10.30998/string.v6i1.9232

Perbandingan Naïve Bayes dan Support Vector Machine untuk Klasifikasi Ulasan Pelanggan Indihome

Aan Rohanah^(1*), Dwi Latifah Rianti⁽²⁾, Betha Nurina Sari⁽³⁾

(1)
(2) Singaperbangsa Karawang University
(3) Singaperbangsa Karawang University
(*) Corresponding Author

Abstract

IndiHome is an internet service provider from PT. Telekomunikasi Indonesia, Tbk with the widest internet coverage in Indonesia. Customer satisfaction is one of the things that must be considered in a company, including the IndiHome company. IndiHome's customer service satisfaction level can be seen from customer reviews via Twitter social media. This study discusses the classification of IndiHome customer reviews by applying the CRISP-DM research stages and the application of the Naïve Bayes Classifier algorithm and the Linear Support Vector Machine Kernel. Customer review data were obtained from Twitter, totaling 1000 tweets using the Rapid Miner and R library tools. The preprocessing stages applied were cleansing, case folding, tokenizing, word conversion, stopword, and stemming. The results of data visualization are presented in the form of a word cloud which is categorized based on positive and negative opinions of words that often appear. The results showed that the application of the Support Vector Machine Kernel Linear algorithm is better than the Naïve Bayes Classifier algorithm with an accuracy value of 82.11%, 76.44% precision, 88.01% recall, and an AUC value of 0.909.

Keywords

IndiHome; Customer Satisfaction; Naïve Bayes Classifier; Support Vector Machine

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References

B. Harto and R. Komalasari, “Optimalisasi Platform Online Internet Marketing untuk Sme Little Rose Bandung,” Empower. Community, vol. 1, no. 1, p. 1, 2020, doi: 10.31543/ecj.v1i1.357.

A. W. Irawan, A. Yusufianto, D. Agustina, and R. Dean, Laporan Survei Internet Apjii 2019-2020 (Q2), vol. 2020. 2020.

R. S. Putra, “Analisis Sentimen Twitter dengan Klasifikasi Naïve Bayes menggunakan Seleksi Fitur Mutual Information dan Inverse Document Frequency,” Skripsi, Departemen Ilmu Komputer, Institut Pertanian Bogor, Bogor, Indonesia, 2017.

N. H. Saputra, “Analisis Sentimen Pemilihan Gubernur Jawa Barat 2018 pada Data Twitter menggunakan Naïve Bayes,” Skripsi, Departemen Ilmu Komputer, Institut Pertanian Bogor, Bogor, Indonesia, 2020.

M. P. Shakina Rizkia, Erwin Budi Setiawan S.Si., M.T, Diyas Puspandari S.S., “Analisis Sentimen Kepuasan Pelanggan Terhadap Internet Provider Indihome di Twitter Menggunakan Metode Decision Tree dan Pembobotan TF-IDF,” e-Proceeding Eng., vol. 6, no. 2, pp. 9683–9693, 2019.

R. Tineges, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM),” J. Media Inform. Budidarma, vol. 4, no. 3, pp. 650–658, 2020, doi: 10.30865/mib.v4i3.2181.

A. O. Murdiansyah and I. Siswanto, “Algoritma Naive Baiyes Classsifier pada Aplikasi Data Mining Berbasis Web,” vol. 1, no. 1, pp. 284–290, 2018.

C. Claudia and A. Budi, “Analisa dan Klasifikasi Sentiment Opini Penonton pada Website IMDB.com dengan Algoritma Support Vector Machine,” J. Inform. dan Bisnis, vol. 6, no. 2, pp. 18–28, 2017.

M. North, Data Mining for the Messes. Infinitte, 2016.

I. P. Windasari, F. N. Uzzi, and K. I. Satoto, “Sentiment analysis on Twitter posts: An analysis of positive or negative opinion on GoJek,” Proc. - 2017 4th Int. Conf. Inf. Technol. Comput. Electr. Eng. ICITACEE 2017, pp. 266–269, 2017, doi: 10.1109/ICITACEE.2017.8257715.

P. R. Sihombing and O. P. Hendarsin, “Perbandingan Metode Artificial Neural Network (ANN) dan Support Vector Machine (SVM) untuk Klasifikasi Kinerja Perusahaan Daerah Air Minum (PDAM) di Indonesia,” J. Ilmu Komputer., vol. 8, no. 1, pp. 9–20, 2020, doi: 10.24843/jik.2020.v13.i01.p02.

DOI: http://dx.doi.org/10.30998/string.v6i1.9232